Evidence map›Paper›PMID 40546950›Full record

ArticleiScience2025

Programmable cell aggregation by a synthetic biosilicification approach.

Qing Wang, Jing Sun, Lei Zang, Huaxiong Yao, Lin Wang, Runtao Zhu, Jie Li, Simin Zeng, Hongting Tang, Teng Wang and 5 more

Abstract read
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Qing WangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Jing SunSchool of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China.
Lei ZangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Huaxiong YaoState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Lin WangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Runtao ZhuState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Jie LiState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Simin ZengState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Hongting TangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Teng WangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Ji LiuDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Bo WangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Bo LiDepartment of Civil Engineering, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China.
Zhiyuan LiuCenter of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Zhuojun DaiState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts have focused on modifying cells with designed organic-based adhesive modules and assembling cells into defined patterns. Here, we present a different approach to guide cell assembly by tuning the organic-inorganic interactions. Our method involves engineering cells to express a silicifying peptide on their surfaces, which promotes silica deposition on the cell walls. This peptide simultaneously binds to the silica synthesized on adjacent cells, triggering cell clustering. The engineered cells exhibit rapid aggregation, with approximately 95% of cells assembling within 15 min. We further show that this capability can facilitate materials assembly and chemical production. Our biosilicification-based approach offers novel insights into natural multicellularity mechanisms and holds potential for applications in biomanufacturing and materials engineering.

Indexed as

BioengineeringBiomaterialsSynthetic biology

Identifiers

PMID40546950
PMCPMC12182305

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.